New Advances and Applications in Statistical Quality Control

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 16783

Special Issue Editors

Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Interests: statistical quality control; reliability; statistics; fuzzy sets
Farmer School of Business, Miami University, Oxford, OH 45056, USA

Special Issue Information

Dear Colleagues,

Statistical quality control (SQC) consists of methods that probably belong to the set of statistical tools which are the most frequently used in practice. For many years, the main aim of research in this area was to propose simple and practical tools, applicable in many areas by users having limited statistical knowledge. Therefore, the proposed procedures have been obtained using many simplifying assumptions that have limited the area of their possible applications.  In recent years, methods of SQC have been implemented in new areas of applications, such as banking and health services. In these new areas, many simplifying assumptions of classical SQC are no longer valid. Therefore, new methods have recently been proposed which rely on advanced methods of mathematical statistics, data mining (including information symmetry), multivariate symmetry and asymmetry, and intelligent computation methods. For this planned Special Issue of Symmetry, we are inviting papers that demonstrate original applications of modern statistical and computational methods in SQC. In particular, we invite papers on the applications of SQC for the analysis of streams of correlated and multivariate data, papers on the applications of SQC for the analysis of functional data (profiles), papers on the applications of SQC for the analysis of streams of imprecisely perceived data (e.g., linguistic data), and papers on intelligent selection (e.g., cost-optimal) of the parameters describing SQC procedures.

Prof. Dr. Olgierd Hryniewicz
Dr. Fadel Megahed
Guest Editors

Manuscript Submission Information

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Keywords

  • monitoring of autocorrelated data
  • monitoring non-stationary streams of data
  • monitoring of multivariate process data
  • selection of process key attributes
  • control charts for functional data
  • control charts for imprecise (linguistic) quality data
  • optimal SQC procedures

Published Papers (8 papers)

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Research

29 pages, 4680 KiB  
Article
A New Hybrid Exponentially Weighted Moving Average Control Chart with Repetitive Sampling for Monitoring the Coefficient of Variation
by Kanita Petcharat, Suvimol Phanyaem and Yupaporn Areepong
Symmetry 2023, 15(5), 999; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15050999 - 28 Apr 2023
Cited by 1 | Viewed by 1215
Abstract
The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification of process irregularities across various fields. This research proposes and [...] Read more.
The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification of process irregularities across various fields. This research proposes and implements a novel SQC methodology in agricultural areas. A control chart is one of the SQC tools that facilitates real-time monitoring of multiple activities, including agricultural yield, industrial yield, and hospital outcomes. Advanced control charts with symmetrical data are being subjected to the new SQC method, which is suitable for this purpose. This research aims to develop a novel hybrid exponentially weighted moving average control chart for detecting the coefficient of variation (CV) using a repetitive sampling method called the HEWMARS-CV control chart. It is an effective tool for monitoring the mean and variance of a process simultaneously. The HEWMARS-CV control chart used the repetitive sampling scheme to generate two pairs of control limits to enhance the performance of the control chart. The proposed control chart is compared with the classical HEWMA and Shewhart control charts regarding the average run length (ARL) when the data has a normal distribution. The Monte Carlo simulation method is utilized to approximate the ARL values of the proposed control charts to determine their performance. The proposed control chart detects small shifts in CV values more effectively than the existing control chart. An illustrative application related to monitor the wheat yield at Rothamsted Experimental Station in Great Britain is also incorporated to demonstrate the efficiency of the proposed control chart. The efficiency of the proposed HEWMARS-CV control chart on the real data shows that the proposed control chart can detect a shift in the CV of the process, and it is superior to the existing control chart in terms of the average run length. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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12 pages, 349 KiB  
Article
Control Charts for Monitoring the Mean of Skew-Normal Samples
by Víctor Hugo Morales and Carlos Arturo Panza
Symmetry 2022, 14(11), 2302; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14112302 - 03 Nov 2022
Cited by 2 | Viewed by 1236
Abstract
The presence of asymmetric data in production processes or service operations has prompted the development of new monitoring schemes. In this article, an adapted version of the exponentially weighted moving averages (EWMA) control chart with dynamic limits is proposed to monitor the mean [...] Read more.
The presence of asymmetric data in production processes or service operations has prompted the development of new monitoring schemes. In this article, an adapted version of the exponentially weighted moving averages (EWMA) control chart with dynamic limits is proposed to monitor the mean of samples from the skew-normal distribution. The detection ability of the proposed control chart in online monitoring was investigated by simulating the average run length (ARL) performance for different out-of-control scenarios. The results of the simulation study suggest that the proposed scheme overcomes the main drawback of the recently developed Shewhart-type control scheme. As shown in this article, the existing Shewhart-type procedure exhibits the undesirable property of taking longer to detect changes in the mean value of skewed normal observations due to increases in the shape parameter of the basic distribution than in stable conditions. The proposed control chart was shown to work fairly acceptably in all considered out-of-control scenarios. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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15 pages, 317 KiB  
Article
Deep Learning-Based Residual Control Chart for Binary Response
by Jong Min Kim and Il Do Ha
Symmetry 2021, 13(8), 1389; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081389 - 31 Jul 2021
Cited by 3 | Viewed by 1818
Abstract
A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning [...] Read more.
A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neural network model and deep learning model. We prove that the deep learning r control chart is relatively efficient to monitor the simulated and real binary response asymmetric data compared with r control chart of the generalized linear model (GLM) with probit and logit link functions and neural network r control chart. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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19 pages, 1694 KiB  
Article
The Development of a Heterogeneous MP Data Model Based on the Ontological Approach
by Sergey Porshnev, Andrey Borodin, Olga Ponomareva, Sergey Mirvoda and Olga Chernova
Symmetry 2021, 13(5), 813; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13050813 - 06 May 2021
Cited by 3 | Viewed by 1197
Abstract
The article discusses the approaches providing symmetric access of all industrial production services to the data of business processes of the enterprise by building a single warehouse of heterogeneous data of a metallurgical production. The warehouse is a part of an automated statistic [...] Read more.
The article discusses the approaches providing symmetric access of all industrial production services to the data of business processes of the enterprise by building a single warehouse of heterogeneous data of a metallurgical production. The warehouse is a part of an automated statistic quality control system for the products of a metallurgical enterprise. The article describes an ontological storage model of data coming from various sources of information in the production process. The concept of “a unit of production of metallurgical production” is introduced that is the connecting component of the entire production life cycle of a metallurgical production. The authors propose an ontological model of the production process, in terms of information flows which are formed in an enterprise at each stage of production. Based on the constructed ontological model, the structure of recording an array of information in the heterogeneous data warehouse is justified and formed. Heterogeneous data warehouse forms a single information space of the enterprise, which serves as the basis for analytical analysis throughout the production and decision—making process. For example, timely response to the deviation reasons from the given physical and chemical properties of the finished product. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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28 pages, 450 KiB  
Article
Control Charts for Joint Monitoring of the Lognormal Mean and Standard Deviation
by Wei-Heng Huang
Symmetry 2021, 13(4), 549; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13040549 - 26 Mar 2021
Cited by 2 | Viewed by 1713
Abstract
The Shewhart X¯- and S-charts are most commonly used for monitoring the process mean and variability based on the assumption of normality. However, many process distributions may follow a positively skewed distribution, such as the lognormal distribution. In this study, [...] Read more.
The Shewhart X¯- and S-charts are most commonly used for monitoring the process mean and variability based on the assumption of normality. However, many process distributions may follow a positively skewed distribution, such as the lognormal distribution. In this study, we discuss the construction of three combined X¯- and S-charts for jointly monitoring the lognormal mean and the standard deviation. The simulation results show that the combined lognormal X¯- and S-charts are more effective when the lognormal distribution is more skewed. A real example is used to demonstrate how the combined lognormal X¯- and S-charts can be applied in practice. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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20 pages, 3148 KiB  
Article
GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance
by Arshad Jamal, Tahir Mahmood, Muhamad Riaz and Hassan M. Al-Ahmadi
Symmetry 2021, 13(2), 362; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13020362 - 23 Feb 2021
Cited by 34 | Viewed by 3319
Abstract
Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety [...] Read more.
Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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21 pages, 4443 KiB  
Article
On the Development of Triple Homogeneously Weighted Moving Average Control Chart
by Muhammad Riaz, Zameer Abbas, Hafiz Zafar Nazir and Muhammad Abid
Symmetry 2021, 13(2), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13020360 - 23 Feb 2021
Cited by 15 | Viewed by 1898
Abstract
To detect sustainable changes in the manufacturing processes, memory-type charting schemes are frequently functioning. The recently designed, homogenously weighted moving average (HWMA) technique is effective for identifying substantial changes in the processes. To make the HWMA chart more effective for persistent shifts in [...] Read more.
To detect sustainable changes in the manufacturing processes, memory-type charting schemes are frequently functioning. The recently designed, homogenously weighted moving average (HWMA) technique is effective for identifying substantial changes in the processes. To make the HWMA chart more effective for persistent shifts in the industrial processes, a double HWMA (DHWMA) chart has been proposed recently. This study intends to develop a triple HWMA (THWMA) chart for efficient monitoring of the process mean under zero- and steady-state scenarios. The non-normal effects of monitoring characteristics under in-control situations for heavy-tailed highly skewed and contaminated normal environments are computed under both states. The relative efficiency of the proposed structure is compared with HWMA, DHWMA, exponentially weighted moving average (EWMA), double EWMA, and the more effective triple EWMA control charting schemes. The relative analysis reveals that the proposed THWMA design performs more efficiently than the existing counterparts. An illustrative application related to substrate manufacturing is also incorporated to demonstrate the proposal. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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14 pages, 728 KiB  
Article
The Design of GLR Control Chart for Monitoring the Geometric Observations Using Sequential Sampling Scheme
by Faisal Shahzad, Zhensheng Huang and Ambreen Shafqat
Symmetry 2020, 12(12), 1964; https://0-doi-org.brum.beds.ac.uk/10.3390/sym12121964 - 27 Nov 2020
Cited by 3 | Viewed by 1767
Abstract
The control charts’ design is focused on system forecasting which is important in mathematics and statistics; these techniques are commonly employed in manufacturing industries. The need for a control chart that can conceptualize and identify the symmetric or asymmetric structure of the monitoring [...] Read more.
The control charts’ design is focused on system forecasting which is important in mathematics and statistics; these techniques are commonly employed in manufacturing industries. The need for a control chart that can conceptualize and identify the symmetric or asymmetric structure of the monitoring phase with more than one aspect of the standard attribute is a necessity of industries. The generalized likelihood ratio (GLR) chart is a well-known method to track both the decrease and increase in the mechanism effectively. A control chart, termed as a GLR control chart, is established in this article, focusing on a sequential sampling scheme (the SS GLR chart) to evaluate the geometrically distributed process parameter. The SS GLR chart statistic is examined on a window of past samples. In contexts of the steady-state average time to signal, the output of the SS GLR control chart is analyzed and compared with the non-sequential geometric GLR chart and the cumulative sum (CUSUM) charts. In this article, the optimum parameter options are presented, and regression equations are established to calculate the SS GLR chart limits. Full article
(This article belongs to the Special Issue New Advances and Applications in Statistical Quality Control)
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